In [1]:
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import plotly.express as px
from geopy.geocoders import Nominatim
import folium
from folium.plugins import HeatMap
from warnings import filterwarnings
filterwarnings('ignore')

Data Source: https://www.kaggle.com/datasets/absin7/zomato-bangalore-dataset

In [2]:
res_data = pd.read_csv(r'Enter the absolute path')
In [3]:
res_data.head()
Out[3]:
url address name online_order book_table rate votes phone location rest_type dish_liked cuisines approx_cost(for two people) reviews_list menu_item listed_in(type) listed_in(city)
0 https://www.zomato.com/bangalore/jalsa-banasha... 942, 21st Main Road, 2nd Stage, Banashankari, ... Jalsa Yes Yes 4.1/5 775 080 42297555\r\n+91 9743772233 Banashankari Casual Dining Pasta, Lunch Buffet, Masala Papad, Paneer Laja... North Indian, Mughlai, Chinese 800 [('Rated 4.0', 'RATED\n A beautiful place to ... [] Buffet Banashankari
1 https://www.zomato.com/bangalore/spice-elephan... 2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ... Spice Elephant Yes No 4.1/5 787 080 41714161 Banashankari Casual Dining Momos, Lunch Buffet, Chocolate Nirvana, Thai G... Chinese, North Indian, Thai 800 [('Rated 4.0', 'RATED\n Had been here for din... [] Buffet Banashankari
2 https://www.zomato.com/SanchurroBangalore?cont... 1112, Next to KIMS Medical College, 17th Cross... San Churro Cafe Yes No 3.8/5 918 +91 9663487993 Banashankari Cafe, Casual Dining Churros, Cannelloni, Minestrone Soup, Hot Choc... Cafe, Mexican, Italian 800 [('Rated 3.0', "RATED\n Ambience is not that ... [] Buffet Banashankari
3 https://www.zomato.com/bangalore/addhuri-udupi... 1st Floor, Annakuteera, 3rd Stage, Banashankar... Addhuri Udupi Bhojana No No 3.7/5 88 +91 9620009302 Banashankari Quick Bites Masala Dosa South Indian, North Indian 300 [('Rated 4.0', "RATED\n Great food and proper... [] Buffet Banashankari
4 https://www.zomato.com/bangalore/grand-village... 10, 3rd Floor, Lakshmi Associates, Gandhi Baza... Grand Village No No 3.8/5 166 +91 8026612447\r\n+91 9901210005 Basavanagudi Casual Dining Panipuri, Gol Gappe North Indian, Rajasthani 600 [('Rated 4.0', 'RATED\n Very good restaurant ... [] Buffet Banashankari
In [4]:
res_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 51717 entries, 0 to 51716
Data columns (total 17 columns):
 #   Column                       Non-Null Count  Dtype 
---  ------                       --------------  ----- 
 0   url                          51717 non-null  object
 1   address                      51717 non-null  object
 2   name                         51717 non-null  object
 3   online_order                 51717 non-null  object
 4   book_table                   51717 non-null  object
 5   rate                         43942 non-null  object
 6   votes                        51717 non-null  int64 
 7   phone                        50509 non-null  object
 8   location                     51696 non-null  object
 9   rest_type                    51490 non-null  object
 10  dish_liked                   23639 non-null  object
 11  cuisines                     51672 non-null  object
 12  approx_cost(for two people)  51371 non-null  object
 13  reviews_list                 51717 non-null  object
 14  menu_item                    51717 non-null  object
 15  listed_in(type)              51717 non-null  object
 16  listed_in(city)              51717 non-null  object
dtypes: int64(1), object(16)
memory usage: 6.7+ MB

Checking null values

In [5]:
d = res_data.isna().sum()
d = d[d>0].sort_values(ascending=False).reset_index()
d.columns = ["Column", 'Null_count']
d['percent_Null'] = round((d['Null_count']/res_data.shape[0])*100, 2)
fig = px.bar(data_frame=d, x = 'Column', y = 'percent_Null', title= 'Num values in the original Data',
            labels= {'Column': 'Column Names', 'percent_Null': "Percentage of Null values"})
fig.show()

dish_liked column is having the with most null values, we can't make any recomendation system with this we can drop the colum

In [6]:
res_data.drop(columns=['dish_liked'], inplace=True)
In [7]:
res_data.shape
Out[7]:
(51717, 16)

Converting the required columns into float or int

votes, rate, approx_cost(for two people) are of object types, need to convert them to float or int types for further analysis

In [8]:
res_data.rate.unique()
Out[8]:
array(['4.1/5', '3.8/5', '3.7/5', '3.6/5', '4.6/5', '4.0/5', '4.2/5',
       '3.9/5', '3.1/5', '3.0/5', '3.2/5', '3.3/5', '2.8/5', '4.4/5',
       '4.3/5', 'NEW', '2.9/5', '3.5/5', nan, '2.6/5', '3.8 /5', '3.4/5',
       '4.5/5', '2.5/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5',
       '3.4 /5', '-', '3.6 /5', '4.8/5', '3.9 /5', '4.2 /5', '4.0 /5',
       '4.1 /5', '3.7 /5', '3.1 /5', '2.9 /5', '3.3 /5', '2.8 /5',
       '3.5 /5', '2.7 /5', '2.5 /5', '3.2 /5', '2.6 /5', '4.5 /5',
       '4.3 /5', '4.4 /5', '4.9/5', '2.1/5', '2.0/5', '1.8/5', '4.6 /5',
       '4.9 /5', '3.0 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5',
       '2.1 /5', '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)

Performing ffill opetation on rate

In [9]:
res_data.rate.fillna(method='ffill', inplace=True)
In [10]:
res_data.shape
Out[10]:
(51717, 16)

Every rating is given out of 5, filtering the numerator and converting it into float

replacing 'new' with 0 and ''-'' with zero

In [11]:
res_data['rate'] = res_data['rate'].apply(lambda x: x.split('/')[0])
In [12]:
res_data.replace('NEW', 0, inplace= True)
res_data.replace('-', 0, inplace=True)
In [13]:
res_data['rate']=res_data['rate'].astype(float)
In [14]:
res_data.rate.unique()
Out[14]:
array([4.1, 3.8, 3.7, 3.6, 4.6, 4. , 4.2, 3.9, 3.1, 3. , 3.2, 3.3, 2.8,
       4.4, 4.3, 0. , 2.9, 3.5, 2.6, 3.4, 4.5, 2.5, 2.7, 4.7, 2.4, 2.2,
       2.3, 4.8, 4.9, 2.1, 2. , 1.8])
In [15]:
res_data['approx_cost(for two people)'].unique()
Out[15]:
array(['800', '300', '600', '700', '550', '500', '450', '650', '400',
       '900', '200', '750', '150', '850', '100', '1,200', '350', '250',
       '950', '1,000', '1,500', '1,300', '199', '80', '1,100', '160',
       '1,600', '230', '130', '50', '190', '1,700', nan, '1,400', '180',
       '1,350', '2,200', '2,000', '1,800', '1,900', '330', '2,500',
       '2,100', '3,000', '2,800', '3,400', '40', '1,250', '3,500',
       '4,000', '2,400', '2,600', '120', '1,450', '469', '70', '3,200',
       '60', '560', '240', '360', '6,000', '1,050', '2,300', '4,100',
       '5,000', '3,700', '1,650', '2,700', '4,500', '140'], dtype=object)
In [16]:
res_data.shape
Out[16]:
(51717, 16)

Droping nan values for approx_cost(for two people)

In [17]:
res_data.dropna(subset=['approx_cost(for two people)'],inplace=True)
In [18]:
res_data.shape
Out[18]:
(51371, 16)

removing ','

In [19]:
res_data['approx_cost(for two people)'] = res_data['approx_cost(for two people)'].apply(lambda x: x.replace(',',''))
In [20]:
res_data['approx_cost(for two people)']=res_data['approx_cost(for two people)'].astype(float)
In [21]:
res_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 51371 entries, 0 to 51716
Data columns (total 16 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   url                          51371 non-null  object 
 1   address                      51371 non-null  object 
 2   name                         51371 non-null  object 
 3   online_order                 51371 non-null  object 
 4   book_table                   51371 non-null  object 
 5   rate                         51371 non-null  float64
 6   votes                        51371 non-null  int64  
 7   phone                        50497 non-null  object 
 8   location                     51371 non-null  object 
 9   rest_type                    51167 non-null  object 
 10  cuisines                     51352 non-null  object 
 11  approx_cost(for two people)  51371 non-null  float64
 12  reviews_list                 51371 non-null  object 
 13  menu_item                    51371 non-null  object 
 14  listed_in(type)              51371 non-null  object 
 15  listed_in(city)              51371 non-null  object 
dtypes: float64(2), int64(1), object(13)
memory usage: 6.7+ MB
In [22]:
res_data.head()
Out[22]:
url address name online_order book_table rate votes phone location rest_type cuisines approx_cost(for two people) reviews_list menu_item listed_in(type) listed_in(city)
0 https://www.zomato.com/bangalore/jalsa-banasha... 942, 21st Main Road, 2nd Stage, Banashankari, ... Jalsa Yes Yes 4.1 775 080 42297555\r\n+91 9743772233 Banashankari Casual Dining North Indian, Mughlai, Chinese 800.0 [('Rated 4.0', 'RATED\n A beautiful place to ... [] Buffet Banashankari
1 https://www.zomato.com/bangalore/spice-elephan... 2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ... Spice Elephant Yes No 4.1 787 080 41714161 Banashankari Casual Dining Chinese, North Indian, Thai 800.0 [('Rated 4.0', 'RATED\n Had been here for din... [] Buffet Banashankari
2 https://www.zomato.com/SanchurroBangalore?cont... 1112, Next to KIMS Medical College, 17th Cross... San Churro Cafe Yes No 3.8 918 +91 9663487993 Banashankari Cafe, Casual Dining Cafe, Mexican, Italian 800.0 [('Rated 3.0', "RATED\n Ambience is not that ... [] Buffet Banashankari
3 https://www.zomato.com/bangalore/addhuri-udupi... 1st Floor, Annakuteera, 3rd Stage, Banashankar... Addhuri Udupi Bhojana No No 3.7 88 +91 9620009302 Banashankari Quick Bites South Indian, North Indian 300.0 [('Rated 4.0', "RATED\n Great food and proper... [] Buffet Banashankari
4 https://www.zomato.com/bangalore/grand-village... 10, 3rd Floor, Lakshmi Associates, Gandhi Baza... Grand Village No No 3.8 166 +91 8026612447\r\n+91 9901210005 Basavanagudi Casual Dining North Indian, Rajasthani 600.0 [('Rated 4.0', 'RATED\n Very good restaurant ... [] Buffet Banashankari

Plotting the average ratting for each restaurent

In [23]:
fig = px.bar(data_frame=res_data.groupby('name')['rate'].mean().nlargest(20).reset_index(),
                x = 'name', y = 'rate',
                labels= {'name': "Restaurent Name", 'rate': "Avegrage rating"},
               
             title='Top 20 restaurents with highest average rating')
fig.update_xaxes(automargin = True)
fig.show()

Famous restaurent types

In [24]:
fig = px.bar(data_frame=res_data['rest_type'].value_counts().nlargest(20).to_frame(name="Restaurent_type_count").reset_index(),
                x = 'index',y =  'Restaurent_type_count', width= 900, height= 300,
            labels= {'index': 'Restaurent Types','Restaurent_type_count':'Count'}, title='Top 20 Restaurent Types', template='plotly_dark')

fig.show()

Quick Bites dominating all the types and it is twice casual Dining

Alalysing the online order and table bokking option avaliability

In [25]:
dob = res_data[['online_order', 'book_table']].value_counts().reset_index()
In [26]:
dob.columns = ['online_order', 'book_table', 'count']
In [27]:
res_data.online_order.value_counts()
Out[27]:
Yes    30444
No     20927
Name: online_order, dtype: int64
In [28]:
fig = px.scatter(data_frame=dob, x = 'online_order', y='book_table', size='count', title='Number of restaurents with online order and book_table option',
                template='plotly_white', width=400, height=400)
fig.show()

Restaurents which have online order are most in number

Restaurents which received most number of votes

In [29]:
res_data.groupby('name')[['rate','votes']].max().nlargest(20, columns = ['votes', 'rate']).reset_index()
Out[29]:
name rate votes
0 Byg Brewski Brewing Company 4.9 16832
1 Toit 4.7 14956
2 Truffles 4.7 14726
3 AB's - Absolute Barbecues 4.9 12121
4 The Black Pearl 4.8 10550
5 Big Pitcher 4.7 9300
6 Onesta 4.6 9085
7 Arbor Brewing Company 4.5 8419
8 Empire Restaurant 4.4 8304
9 Prost Brew Pub 4.5 7871
10 Church Street Social 4.3 7584
11 Hoot 4.2 7330
12 Barbeque Nation 4.8 7270
13 Meghana Foods 4.5 7238
14 Flechazo 4.9 7154
15 The Hole in the Wall Cafe 4.6 7137
16 Biergarten 4.8 7064
17 Vapour Pub & Brewery 4.2 6998
18 TBC Sky Lounge 4.7 6745
19 Chili's American Grill & Bar 4.8 6470
In [30]:
fig = px.pie(data_frame=res_data.groupby('name')['votes'].max().nlargest(10).reset_index(),
            names= 'name', values= 'votes', hole= 0.3, )
fig.show()

Number of restaurents in given location

In [31]:
res_data.groupby('location')['name'].unique()
Out[31]:
location
BTM                  [Sankranthi Veg Restaurant, Hearts Unlock Cafe...
Banashankari         [Jalsa, Spice Elephant, San Churro Cafe, Addhu...
Banaswadi            [Cafe Nibras, The Sanctuary, Crunch Pizzas, 9 ...
Bannerghatta Road    [Deja Vu Resto Bar, Fattoush, Empire Restauran...
Basavanagudi         [Grand Village, Timepass Dinner, Srinathji's C...
                                           ...                        
West Bangalore       [FreshMenu, Fit Dish Fetish, Garden City Mobil...
Whitefield           [Imperio Cafe, Night Diaries, LocalHost, AB's ...
Wilson Garden        [Tree Top, Sahana's (Nati Style), Karavali Kol...
Yelahanka            [Prashanth Naati Corner, Red Chillies Curries ...
Yeshwantpur          [Chef's Bank, New Agarwal Bhavan, Fishing Boat...
Name: name, Length: 93, dtype: object
In [32]:
num_res = pd.DataFrame()
In [33]:
num_res['location'] = res_data.groupby('location')['name'].unique().index
In [34]:
res_data.groupby('location')['name'].unique().reset_index()['name'].apply(lambda x: len(x))
Out[34]:
0     698
1     283
2     199
3     447
4     205
     ... 
88      4
89    819
90     49
91      3
92     69
Name: name, Length: 93, dtype: int64
In [35]:
num_res['Restaurents_counts'] = res_data.groupby('location')['name'].unique().reset_index()['name'].apply(lambda x: len(x))
In [36]:
num_res= num_res.sort_values(by = 'Restaurents_counts', ascending = False)
In [37]:
num_res
Out[37]:
location Restaurents_counts
89 Whitefield 819
0 BTM 698
19 Electronic City 695
22 HSR 681
56 Marathahalli 656
... ... ...
50 Langford Town 2
68 Rajarajeshwari Nagar 2
30 Jakkur 1
64 Peenya 1
39 Kengeri 1

93 rows × 2 columns

In [38]:
fig = px.bar(data_frame=num_res.head(20), y = 'location',
            x = 'Restaurents_counts', orientation='h', title='Top 20 Areas with most Restaurents')
fig.show()

Approx for for 2 people

Distribution of approx cost

In [39]:
res_data['approx_cost(for two people)'].describe()
Out[39]:
count    51371.000000
mean       555.431566
std        438.850728
min         40.000000
25%        300.000000
50%        400.000000
75%        650.000000
max       6000.000000
Name: approx_cost(for two people), dtype: float64
In [40]:
fig = px.histogram(data_frame= res_data['approx_cost(for two people)'], marginal='box',
                  labels= {'value': 'Approx cost for 2 people(Price)'})
fig.show()

Cost vs Rating

In [41]:
fig = px.scatter(data_frame=res_data, x = 'rate', y = 'approx_cost(for two people)',color = 'online_order', template='ygridoff')
fig.show()

Restaurents with more than 4 rating tend to have higher cost and most of the restaurents do not accept online order, restaurents with rating more than 4 is accepting the online orders

In [42]:
res_data['online_order'].value_counts()
Out[42]:
Yes    30444
No     20927
Name: online_order, dtype: int64

Difference between prices of restaurents that accept online and do not accept online order

In [43]:
fig = px.violin(data_frame=res_data, x = 'online_order', y = 'approx_cost(for two people)', title='Online order vs cost', template='plotly_dark')
fig.show()

Even though the mean price is same, the restaurents which donot accept the online order is having higher price

Most costly restaurent

In [44]:
res_data[res_data['approx_cost(for two people)'] == res_data['approx_cost(for two people)'].max()].loc[:, 'name'].value_counts()
Out[44]:
Le Cirque Signature - The Leela Palace    2
Name: name, dtype: int64

Most cheap restaurent

In [45]:
res_data[res_data['approx_cost(for two people)'] == res_data['approx_cost(for two people)'].min()].loc[:, 'name'].value_counts()
Out[45]:
Srinidhi Sagar Food Line    4
Srinidhi Sagar              2
Srinidhi Sagar Deluxe       2
Name: name, dtype: int64

Restaurents with large number of outlets

In [46]:
import plotly.graph_objects as go

fig = go.Figure(data=[go.Table(header=dict(values=['Restaurent_Name', 'Number of outlets']),
                 cells=dict(values=[res_data.groupby(['name']).agg(['count'])['address'].reset_index().nlargest(20, columns = 'count')['name'].to_list()
                                    , res_data.groupby(['name']).agg(['count'])['address'].reset_index().nlargest(20, columns = 'count')['count'].to_list()]))
                     ])
fig.update_layout(template = 'plotly_dark')
fig.show()
In [47]:
res_data.head()
Out[47]:
url address name online_order book_table rate votes phone location rest_type cuisines approx_cost(for two people) reviews_list menu_item listed_in(type) listed_in(city)
0 https://www.zomato.com/bangalore/jalsa-banasha... 942, 21st Main Road, 2nd Stage, Banashankari, ... Jalsa Yes Yes 4.1 775 080 42297555\r\n+91 9743772233 Banashankari Casual Dining North Indian, Mughlai, Chinese 800.0 [('Rated 4.0', 'RATED\n A beautiful place to ... [] Buffet Banashankari
1 https://www.zomato.com/bangalore/spice-elephan... 2nd Floor, 80 Feet Road, Near Big Bazaar, 6th ... Spice Elephant Yes No 4.1 787 080 41714161 Banashankari Casual Dining Chinese, North Indian, Thai 800.0 [('Rated 4.0', 'RATED\n Had been here for din... [] Buffet Banashankari
2 https://www.zomato.com/SanchurroBangalore?cont... 1112, Next to KIMS Medical College, 17th Cross... San Churro Cafe Yes No 3.8 918 +91 9663487993 Banashankari Cafe, Casual Dining Cafe, Mexican, Italian 800.0 [('Rated 3.0', "RATED\n Ambience is not that ... [] Buffet Banashankari
3 https://www.zomato.com/bangalore/addhuri-udupi... 1st Floor, Annakuteera, 3rd Stage, Banashankar... Addhuri Udupi Bhojana No No 3.7 88 +91 9620009302 Banashankari Quick Bites South Indian, North Indian 300.0 [('Rated 4.0', "RATED\n Great food and proper... [] Buffet Banashankari
4 https://www.zomato.com/bangalore/grand-village... 10, 3rd Floor, Lakshmi Associates, Gandhi Baza... Grand Village No No 3.8 166 +91 8026612447\r\n+91 9901210005 Basavanagudi Casual Dining North Indian, Rajasthani 600.0 [('Rated 4.0', 'RATED\n Very good restaurant ... [] Buffet Banashankari
In [48]:
res_data['listed_in(type)'].value_counts().reset_index()
Out[48]:
index listed_in(type)
0 Delivery 25786
1 Dine-out 17664
2 Desserts 3568
3 Cafes 1703
4 Drinks & nightlife 1091
5 Buffet 871
6 Pubs and bars 688

Restaurent types

In [49]:
fig = px.bar(data_frame=res_data['listed_in(type)'].value_counts().reset_index(), x = 'index', y = 'listed_in(type)', 
      labels = {'index': "Restaurent Type", "listed_in(type)": 'Count of Restaurent type'}, title = 'Restaurent types and their count in Banglore')
fig.update_layout(width = 500, height = 300, template = 'seaborn')
fig.show()

Most cooking styles in Banglore for restaurents

In [50]:
res_data.cuisines.value_counts().reset_index()
Out[50]:
index cuisines
0 North Indian 2872
1 North Indian, Chinese 2377
2 South Indian 1822
3 Biryani 915
4 Bakery, Desserts 903
... ... ...
2710 Fast Food, Chinese, Burger, Hot dogs, Sandwich 1
2711 North Indian, Mughlai, Chinese, Seafood, South... 1
2712 Seafood, Continental, European, Mediterranean 1
2713 Fast Food, Sandwich, Salad, Healthy Food 1
2714 North Indian, Chinese, Arabian, Momos 1

2715 rows × 2 columns

In [51]:
fig = px.treemap(data_frame=res_data.cuisines.value_counts().nlargest(50).reset_index(), path= ['index'], values='cuisines')
fig.update_layout(title = 'Banglore Restaurents cooking styles')
fig.show()

North Indian style of food is mostly dominated in Banglore

Geographical analysis

In [52]:
res_data.location.unique()
Out[52]:
array(['Banashankari', 'Basavanagudi', 'Mysore Road', 'Jayanagar',
       'Kumaraswamy Layout', 'Rajarajeshwari Nagar', 'Vijay Nagar',
       'Uttarahalli', 'JP Nagar', 'South Bangalore', 'City Market',
       'Nagarbhavi', 'Bannerghatta Road', 'BTM', 'Kanakapura Road',
       'Bommanahalli', 'CV Raman Nagar', 'Electronic City', 'HSR',
       'Marathahalli', 'Sarjapur Road', 'Wilson Garden', 'Shanti Nagar',
       'Koramangala 5th Block', 'Koramangala 8th Block', 'Richmond Road',
       'Koramangala 7th Block', 'Jalahalli', 'Koramangala 4th Block',
       'Bellandur', 'Whitefield', 'East Bangalore', 'Old Airport Road',
       'Indiranagar', 'Koramangala 1st Block', 'Frazer Town', 'RT Nagar',
       'MG Road', 'Brigade Road', 'Lavelle Road', 'Church Street',
       'Ulsoor', 'Residency Road', 'Shivajinagar', 'Infantry Road',
       'St. Marks Road', 'Cunningham Road', 'Race Course Road',
       'Commercial Street', 'Vasanth Nagar', 'HBR Layout', 'Domlur',
       'Ejipura', 'Jeevan Bhima Nagar', 'Old Madras Road', 'Malleshwaram',
       'Seshadripuram', 'Kammanahalli', 'Koramangala 6th Block',
       'Majestic', 'Langford Town', 'Central Bangalore', 'Sanjay Nagar',
       'Brookefield', 'ITPL Main Road, Whitefield',
       'Varthur Main Road, Whitefield', 'KR Puram',
       'Koramangala 2nd Block', 'Koramangala 3rd Block', 'Koramangala',
       'Hosur Road', 'Rajajinagar', 'Banaswadi', 'North Bangalore',
       'Nagawara', 'Hennur', 'Kalyan Nagar', 'New BEL Road', 'Jakkur',
       'Rammurthy Nagar', 'Thippasandra', 'Kaggadasapura', 'Hebbal',
       'Kengeri', 'Sankey Road', 'Sadashiv Nagar', 'Basaveshwara Nagar',
       'Yeshwantpur', 'West Bangalore', 'Magadi Road', 'Yelahanka',
       'Sahakara Nagar', 'Peenya'], dtype=object)
In [56]:
from geopy.geocoders import Nominatim
geolocator = Nominatim(user_agent="app")
loc = geolocator.geocode('CV Raman Nagar')
loc
Out[56]:
Location(CV Raman, Wyra Road, Kaviraj Nagar, Khammam, Khammam_Urban mandal, Khammam, Telangana, 507002, India, (17.2510682, 80.1651978, 0.0))

It is giving us the data of Telengana state

In [57]:
loc = geolocator.geocode('CV Raman Nagar, Banglore')
print(loc)

None
In [58]:
loc = geolocator.geocode('Banashankari, Bangalore')
print(loc)

Banashankari, Kanakapura Road, Kadirenahalli, Banashankari Temple ward, South Zone, Bengaluru, Bangalore South, Bangalore Urban, Karnataka, 560070", India
In [59]:
loc = geolocator.geocode('Kalyan Nagar, Hyderabad')
print(loc)

Kalyan Nagar, Ward 101 Erragadda, Greater Hyderabad Municipal Corporation Central Zone, Hyderabad, Balanagar mandal, Medchal–Malkajgiri, Telangana, 500018, India

Appending Bangalore at the end for location column

In [60]:
location_df = pd.DataFrame({"Location": res_data['location'].unique()})
In [61]:
location_df.head()
Out[61]:
Location
0 Banashankari
1 Basavanagudi
2 Mysore Road
3 Jayanagar
4 Kumaraswamy Layout
In [62]:
location_df['Location_State'] = location_df['Location'] + ", "+ "Bangalore"
In [63]:
location_df.head()
Out[63]:
Location Location_State
0 Banashankari Banashankari, Bangalore
1 Basavanagudi Basavanagudi, Bangalore
2 Mysore Road Mysore Road, Bangalore
3 Jayanagar Jayanagar, Bangalore
4 Kumaraswamy Layout Kumaraswamy Layout, Bangalore
In [64]:
def get_lat_long(x):
    """
    Return the latitude and longitude for a given location
    x: input str, takes the location as an input
    """
    loc = geolocator.geocode(x)
    if loc is None:
        return np.nan
    else:
        return (loc.latitude, loc.longitude)
In [65]:
location_df['Latitude_Longitude'] = location_df['Location_State'].apply(get_lat_long)
In [66]:
location_df.head()
Out[66]:
Location Location_State Latitude_Longitude
0 Banashankari Banashankari, Bangalore (12.9152208, 77.573598)
1 Basavanagudi Basavanagudi, Bangalore (12.9417261, 77.5755021)
2 Mysore Road Mysore Road, Bangalore (12.9569278, 77.5497675)
3 Jayanagar Jayanagar, Bangalore (12.9292731, 77.5824229)
4 Kumaraswamy Layout Kumaraswamy Layout, Bangalore (12.9081487, 77.5553179)
In [67]:
location_df.isna().sum()
Out[67]:
Location              0
Location_State        0
Latitude_Longitude    1
dtype: int64
In [68]:
location_df[location_df.isna().any(axis=1)]
Out[68]:
Location Location_State Latitude_Longitude
79 Rammurthy Nagar Rammurthy Nagar, Bangalore NaN
In [69]:
loc = geolocator.geocode('Rammurthy Nagar, Bangalore')
print(loc)

None

Speperating latitude and longitude columns

In [70]:
out = location_df['Latitude_Longitude'].apply(pd.Series)
In [71]:
out.columns = ['Latitude', 'Longitude']
In [72]:
location_df['Latitude'] = out['Latitude']
location_df['Longitude'] = out['Longitude']
In [73]:
location_df
Out[73]:
Location Location_State Latitude_Longitude Latitude Longitude
0 Banashankari Banashankari, Bangalore (12.9152208, 77.573598) 12.915221 77.573598
1 Basavanagudi Basavanagudi, Bangalore (12.9417261, 77.5755021) 12.941726 77.575502
2 Mysore Road Mysore Road, Bangalore (12.9569278, 77.5497675) 12.956928 77.549768
3 Jayanagar Jayanagar, Bangalore (12.9292731, 77.5824229) 12.929273 77.582423
4 Kumaraswamy Layout Kumaraswamy Layout, Bangalore (12.9081487, 77.5553179) 12.908149 77.555318
... ... ... ... ... ...
88 West Bangalore West Bangalore, Bangalore (12.9848519, 77.5400626) 12.984852 77.540063
89 Magadi Road Magadi Road, Bangalore (12.9882076, 77.5113613) 12.988208 77.511361
90 Yelahanka Yelahanka, Bangalore (13.1006982, 77.5963454) 13.100698 77.596345
91 Sahakara Nagar Sahakara Nagar, Bangalore (13.0621474, 77.58006135480495) 13.062147 77.580061
92 Peenya Peenya, Bangalore (13.0329419, 77.5273253) 13.032942 77.527325

93 rows × 5 columns

In [74]:
locations = res_data['location'].value_counts().reset_index()
locations.columns = ['Location', 'Count']
In [75]:
locations
Out[75]:
Location Count
0 BTM 5098
1 HSR 2508
2 Koramangala 5th Block 2481
3 JP Nagar 2230
4 Whitefield 2133
... ... ...
88 West Bangalore 6
89 Yelahanka 5
90 Jakkur 3
91 Rajarajeshwari Nagar 2
92 Peenya 1

93 rows × 2 columns

In [76]:
location_df = locations.merge(location_df, on = 'Location', how = 'left').dropna()
In [77]:
location_df
Out[77]:
Location Count Location_State Latitude_Longitude Latitude Longitude
0 BTM 5098 BTM, Bangalore (12.911275849999999, 77.60456543431182) 12.911276 77.604565
1 HSR 2508 HSR, Bangalore (12.9116225, 77.6388622) 12.911623 77.638862
2 Koramangala 5th Block 2481 Koramangala 5th Block, Bangalore (12.9348429, 77.6189768) 12.934843 77.618977
3 JP Nagar 2230 JP Nagar, Bangalore (12.9072515, 77.5782713) 12.907251 77.578271
4 Whitefield 2133 Whitefield, Bangalore (12.9696365, 77.7497448) 12.969637 77.749745
... ... ... ... ... ... ...
88 West Bangalore 6 West Bangalore, Bangalore (12.9848519, 77.5400626) 12.984852 77.540063
89 Yelahanka 5 Yelahanka, Bangalore (13.1006982, 77.5963454) 13.100698 77.596345
90 Jakkur 3 Jakkur, Bangalore (13.0784743, 77.6068938) 13.078474 77.606894
91 Rajarajeshwari Nagar 2 Rajarajeshwari Nagar, Bangalore (12.9274413, 77.5155224) 12.927441 77.515522
92 Peenya 1 Peenya, Bangalore (13.0329419, 77.5273253) 13.032942 77.527325

92 rows × 6 columns

constructing Base map for Banglorre location

In [78]:
loc = geolocator.geocode('Bangalore, Karnataka')
print(loc.latitude, loc.longitude)

12.9767936 77.590082
In [79]:
import folium
In [80]:
basemap = folium.Map(location= [12.9767936, 77.590082])
In [81]:
from branca.element import Figure
fig1=Figure(width=750,height=300)
basemap = folium.Map(location= [12.9767936, 77.590082])
fig1.add_child(basemap)
folium.TileLayer('Stamen Terrain').add_to(basemap)
folium.TileLayer('Stamen Toner').add_to(basemap)
folium.TileLayer('Stamen Water Color').add_to(basemap)
folium.TileLayer('cartodbpositron').add_to(basemap)
folium.TileLayer('cartodbdark_matter').add_to(basemap)
folium.LayerControl().add_to(basemap)
folium.Marker(location=[12.9767936, 77.590082],tooltip='Bangalore, Karnataka').add_to(basemap)
basemap
Out[81]:
In [82]:
def get_basemap(default_loc = [12.9767936, 77.590082], width = 750, height = 500, tooltip = "Bangalore", zoom = 12 ):
    """
    This will genetrate the Base map with default customization
    """
    fig1=Figure(width=width,height=height)
    basemap = folium.Map(location= default_loc, zoom_start=zoom)
    fig1.add_child(basemap)
    folium.Marker(location=default_loc, tooltip=f'{tooltip}').add_to(basemap)
    return basemap
In [83]:
loc_base = get_basemap()
In [84]:
loc_base
Out[84]:
In [85]:
markers = location_df[['Latitude', 'Longitude', 'Location']]
In [86]:
len(markers)
Out[86]:
92
In [87]:
for i in range(0,len(markers)):
    folium.Marker(
      location=[markers.iloc[i]['Latitude'], markers.iloc[i]['Longitude']],
      popup=markers.iloc[i]['Location'],
   ).add_to(loc_base)

Restaurents Locations in Bangalore

In [88]:
loc_base
Out[88]:

Finding locations of Cafe Coffee Day

In [89]:
cafe_cofee_day = res_data[res_data.name == 'Cafe Coffee Day']
In [90]:
cafe_cofee_day["Location_state"] = cafe_cofee_day['location'] + ", "+"Bangalore"
In [91]:
cafe_cofee_day = cafe_cofee_day[['name','location', 'Location_state']]
In [92]:
cafe_cofee_day.drop_duplicates(subset='location', inplace=True, keep = 'first')
In [93]:
cafe_cofee_day.reset_index(drop = True, inplace = True)
In [94]:
cafe_cofee_day
Out[94]:
name location Location_state
0 Cafe Coffee Day Banashankari Banashankari, Bangalore
1 Cafe Coffee Day Jayanagar Jayanagar, Bangalore
2 Cafe Coffee Day Bannerghatta Road Bannerghatta Road, Bangalore
3 Cafe Coffee Day Basavanagudi Basavanagudi, Bangalore
4 Cafe Coffee Day Wilson Garden Wilson Garden, Bangalore
5 Cafe Coffee Day Bellandur Bellandur, Bangalore
6 Cafe Coffee Day Sarjapur Road Sarjapur Road, Bangalore
7 Cafe Coffee Day Brigade Road Brigade Road, Bangalore
8 Cafe Coffee Day Race Course Road Race Course Road, Bangalore
9 Cafe Coffee Day Brookefield Brookefield, Bangalore
10 Cafe Coffee Day Marathahalli Marathahalli, Bangalore
11 Cafe Coffee Day BTM BTM, Bangalore
12 Cafe Coffee Day Lavelle Road Lavelle Road, Bangalore
13 Cafe Coffee Day Electronic City Electronic City, Bangalore
14 Cafe Coffee Day Frazer Town Frazer Town, Bangalore
15 Cafe Coffee Day HSR HSR, Bangalore
16 Cafe Coffee Day Indiranagar Indiranagar, Bangalore
17 Cafe Coffee Day JP Nagar JP Nagar, Bangalore
18 Cafe Coffee Day Kalyan Nagar Kalyan Nagar, Bangalore
19 Cafe Coffee Day Nagawara Nagawara, Bangalore
20 Cafe Coffee Day Kammanahalli Kammanahalli, Bangalore
21 Cafe Coffee Day Koramangala 5th Block Koramangala 5th Block, Bangalore
22 Cafe Coffee Day Koramangala 6th Block Koramangala 6th Block, Bangalore
23 Cafe Coffee Day Koramangala 7th Block Koramangala 7th Block, Bangalore
24 Cafe Coffee Day Malleshwaram Malleshwaram, Bangalore
25 Cafe Coffee Day New BEL Road New BEL Road, Bangalore
26 Cafe Coffee Day MG Road MG Road, Bangalore
27 Cafe Coffee Day Old Airport Road Old Airport Road, Bangalore
28 Cafe Coffee Day Rajajinagar Rajajinagar, Bangalore
29 Cafe Coffee Day Vijay Nagar Vijay Nagar, Bangalore
30 Cafe Coffee Day Basaveshwara Nagar Basaveshwara Nagar, Bangalore
31 Cafe Coffee Day Residency Road Residency Road, Bangalore
32 Cafe Coffee Day Seshadripuram Seshadripuram, Bangalore
33 Cafe Coffee Day Whitefield Whitefield, Bangalore
In [95]:
cafe_cofee_day['Latitude_Longitude'] = cafe_cofee_day['Location_state'].apply(get_lat_long)
In [96]:
cafe_cofee_day.isna().sum()
Out[96]:
name                  0
location              0
Location_state        0
Latitude_Longitude    0
dtype: int64
In [97]:
out  = cafe_cofee_day['Latitude_Longitude'].apply(pd.Series)
out.columns = ['Latitude', 'Longitude']
In [98]:
cafe_cofee_day['Latitude'] = out['Latitude']
cafe_cofee_day['Longitude'] = out['Longitude']
In [99]:
cafe_cofee_day
Out[99]:
name location Location_state Latitude_Longitude Latitude Longitude
0 Cafe Coffee Day Banashankari Banashankari, Bangalore (12.9152208, 77.573598) 12.915221 77.573598
1 Cafe Coffee Day Jayanagar Jayanagar, Bangalore (12.9292731, 77.5824229) 12.929273 77.582423
2 Cafe Coffee Day Bannerghatta Road Bannerghatta Road, Bangalore (12.8545439, 77.5886892) 12.854544 77.588689
3 Cafe Coffee Day Basavanagudi Basavanagudi, Bangalore (12.9417261, 77.5755021) 12.941726 77.575502
4 Cafe Coffee Day Wilson Garden Wilson Garden, Bangalore (12.9489339, 77.5968273) 12.948934 77.596827
5 Cafe Coffee Day Bellandur Bellandur, Bangalore (12.93103185, 77.6782471) 12.931032 77.678247
6 Cafe Coffee Day Sarjapur Road Sarjapur Road, Bangalore (12.924299, 77.6517653) 12.924299 77.651765
7 Cafe Coffee Day Brigade Road Brigade Road, Bangalore (12.9736132, 77.6074716) 12.973613 77.607472
8 Cafe Coffee Day Race Course Road Race Course Road, Bangalore (12.9838271, 77.5853279) 12.983827 77.585328
9 Cafe Coffee Day Brookefield Brookefield, Bangalore (12.9668213, 77.7168891) 12.966821 77.716889
10 Cafe Coffee Day Marathahalli Marathahalli, Bangalore (12.9552572, 77.6984163) 12.955257 77.698416
11 Cafe Coffee Day BTM BTM, Bangalore (12.911275849999999, 77.60456543431182) 12.911276 77.604565
12 Cafe Coffee Day Lavelle Road Lavelle Road, Bangalore (12.9683054, 77.5964883) 12.968305 77.596488
13 Cafe Coffee Day Electronic City Electronic City, Bangalore (12.848759900000001, 77.64825295827616) 12.848760 77.648253
14 Cafe Coffee Day Frazer Town Frazer Town, Bangalore (12.996845, 77.6130165) 12.996845 77.613017
15 Cafe Coffee Day HSR HSR, Bangalore (12.9116225, 77.6388622) 12.911623 77.638862
16 Cafe Coffee Day Indiranagar Indiranagar, Bangalore (12.9732913, 77.6404672) 12.973291 77.640467
17 Cafe Coffee Day JP Nagar JP Nagar, Bangalore (12.9072515, 77.5782713) 12.907251 77.578271
18 Cafe Coffee Day Kalyan Nagar Kalyan Nagar, Bangalore (13.0221416, 77.6403368) 13.022142 77.640337
19 Cafe Coffee Day Nagawara Nagawara, Bangalore (13.0412234, 77.6249525) 13.041223 77.624953
20 Cafe Coffee Day Kammanahalli Kammanahalli, Bangalore (13.0093455, 77.6377094) 13.009346 77.637709
21 Cafe Coffee Day Koramangala 5th Block Koramangala 5th Block, Bangalore (12.9348429, 77.6189768) 12.934843 77.618977
22 Cafe Coffee Day Koramangala 6th Block Koramangala 6th Block, Bangalore (12.9390255, 77.6238477) 12.939025 77.623848
23 Cafe Coffee Day Koramangala 7th Block Koramangala 7th Block, Bangalore (12.9364846, 77.6134783) 12.936485 77.613478
24 Cafe Coffee Day Malleshwaram Malleshwaram, Bangalore (13.0027353, 77.5703253) 13.002735 77.570325
25 Cafe Coffee Day New BEL Road New BEL Road, Bangalore (13.0290931, 77.5710622) 13.029093 77.571062
26 Cafe Coffee Day MG Road MG Road, Bangalore (12.9755264, 77.6067902) 12.975526 77.606790
27 Cafe Coffee Day Old Airport Road Old Airport Road, Bangalore (12.9593577, 77.6614182) 12.959358 77.661418
28 Cafe Coffee Day Rajajinagar Rajajinagar, Bangalore (12.9882338, 77.554883) 12.988234 77.554883
29 Cafe Coffee Day Vijay Nagar Vijay Nagar, Bangalore (12.96595445, 77.61253320881664) 12.965954 77.612533
30 Cafe Coffee Day Basaveshwara Nagar Basaveshwara Nagar, Bangalore (12.9932236, 77.5391579) 12.993224 77.539158
31 Cafe Coffee Day Residency Road Residency Road, Bangalore (13.052791, 77.6203393) 13.052791 77.620339
32 Cafe Coffee Day Seshadripuram Seshadripuram, Bangalore (12.9931876, 77.5753419) 12.993188 77.575342
33 Cafe Coffee Day Whitefield Whitefield, Bangalore (12.9696365, 77.7497448) 12.969637 77.749745
In [100]:
cafe_cofee_day_base = get_basemap()
In [101]:
cafe_cofee_day_base
Out[101]:
In [102]:
for i in range(0,len(cafe_cofee_day)):
    folium.Marker(
      location=[cafe_cofee_day.iloc[i]['Latitude'], cafe_cofee_day.iloc[i]['Longitude']],
      popup=markers.iloc[i]['Location'],
   ).add_to(cafe_cofee_day_base)

All the locations of cafe Cofee Day

In [103]:
cafe_cofee_day_base
Out[103]:

Same applies for all the other locations, create a function to do this

Heat map for the locations

Heat map for count of restaurents in the location

In [104]:
from folium.plugins import HeatMap
In [105]:
all_loc_base_heat = get_basemap()
In [106]:
HeatMap(location_df[['Latitude','Longitude','Count']].values.tolist(),zoom=20,radius=15).add_to(all_loc_base_heat)
Out[106]:
<folium.plugins.heat_map.HeatMap at 0x235a022a748>
In [107]:
all_loc_base_heat
Out[107]:

Restaurents tents to be concentrated in the central banglore, By refering to this we can understant with area is most suitable for new restaurents